Instructions to use internlm/internlm2-math-plus-7b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use internlm/internlm2-math-plus-7b with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="internlm/internlm2-math-plus-7b", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("internlm/internlm2-math-plus-7b", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use internlm/internlm2-math-plus-7b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "internlm/internlm2-math-plus-7b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "internlm/internlm2-math-plus-7b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/internlm/internlm2-math-plus-7b

SGLang

How to use internlm/internlm2-math-plus-7b with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "internlm/internlm2-math-plus-7b" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "internlm/internlm2-math-plus-7b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "internlm/internlm2-math-plus-7b" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "internlm/internlm2-math-plus-7b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use internlm/internlm2-math-plus-7b with Docker Model Runner:
```
docker model run hf.co/internlm/internlm2-math-plus-7b
```

internlm2-math-plus-7b

Commit History

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Commit History

Update EvalEval source links to flat datastore 9e8cd97 verified

Add EvalEval community eval results 387fe70 verified

Add EvalEval community eval results 83d1213 verified

Add EvalEval community eval results f1f7da2 verified

replace get_max_length 2565e42

update modeling file to newest 1e010ec

Update config.json b9beabf verified

Create configuration_internlm2.py ffa2a16 verified

Delete configuration_internlm.py c5cf94d verified

Update config.json 2b67ca8 verified

Upload folder using huggingface_hub 44cc4a3 verified

initial commit 84a683b verified

Update EvalEval source links to flat datastore

9e8cd97
verified

Add EvalEval community eval results

387fe70
verified

Add EvalEval community eval results

83d1213
verified

Add EvalEval community eval results

f1f7da2
verified

replace get_max_length

2565e42

update modeling file to newest

1e010ec

Update config.json

b9beabf
verified

Create configuration_internlm2.py

ffa2a16
verified

Delete configuration_internlm.py

c5cf94d
verified

Update config.json

2b67ca8
verified

Upload folder using huggingface_hub

44cc4a3
verified

initial commit

84a683b
verified